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/*
* ******************************************************************************
* *
* *
* * This program and the accompanying materials are made available under the
* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * Unless required by applicable law or agreed to in writing, software
* * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* * License for the specific language governing permissions and limitations
* * under the License.
* *
* * SPDX-License-Identifier: Apache-2.0
* *****************************************************************************
*/
package org.deeplearning4j.nn.conf;
import lombok.*;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.conf.memory.NetworkMemoryReport;
import org.deeplearning4j.nn.conf.serde.JsonMappers;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.util.OutputLayerUtil;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.linalg.lossfunctions.impl.LossBinaryXENT;
import org.nd4j.linalg.lossfunctions.impl.LossMCXENT;
import org.nd4j.linalg.lossfunctions.impl.LossMSE;
import org.nd4j.linalg.lossfunctions.impl.LossNegativeLogLikelihood;
import org.nd4j.shade.jackson.databind.JsonNode;
import org.nd4j.shade.jackson.databind.ObjectMapper;
import org.nd4j.shade.jackson.databind.exc.InvalidTypeIdException;
import org.nd4j.shade.jackson.databind.node.ArrayNode;
import java.io.IOException;
import java.io.Serializable;
import java.util.*;
@Data
@AllArgsConstructor(access = AccessLevel.PRIVATE)
@NoArgsConstructor
@Slf4j
public class MultiLayerConfiguration implements Serializable, Cloneable {
protected List confs;
protected Map inputPreProcessors = new HashMap<>();
protected BackpropType backpropType = BackpropType.Standard;
protected int tbpttFwdLength = 20;
protected int tbpttBackLength = 20;
protected boolean validateOutputLayerConfig = true; //Default to legacy for pre 1.0.0-beta3 networks on deserialization
@Getter
@Setter
protected WorkspaceMode trainingWorkspaceMode = WorkspaceMode.ENABLED;
@Getter
@Setter
protected WorkspaceMode inferenceWorkspaceMode = WorkspaceMode.ENABLED;
@Getter
@Setter
protected CacheMode cacheMode;
@Getter
@Setter
protected DataType dataType = DataType.FLOAT; //Default to float for deserialization of beta3 and earlier nets
//Counter for the number of parameter updates so far
// This is important for learning rate schedules, for example, and is stored here to ensure it is persisted
// for Spark and model serialization
protected int iterationCount = 0;
//Counter for the number of epochs completed so far. Used for per-epoch schedules
protected int epochCount = 0;
public int getEpochCount() {
return epochCount;
}
public void setEpochCount(int epochCount) {
this.epochCount = epochCount;
for (int i = 0; i < confs.size(); i++) {
getConf(i).setEpochCount(epochCount);
}
}
/**
* @return JSON representation of NN configuration
*/
public String toYaml() {
ObjectMapper mapper = NeuralNetConfiguration.mapperYaml();
synchronized (mapper) {
try {
return mapper.writeValueAsString(this);
} catch (org.nd4j.shade.jackson.core.JsonProcessingException e) {
throw new RuntimeException(e);
}
}
}
/**
* Create a neural net configuration from json
*
* @param json the neural net configuration from json
* @return {@link MultiLayerConfiguration}
*/
public static MultiLayerConfiguration fromYaml(String json) {
ObjectMapper mapper = NeuralNetConfiguration.mapperYaml();
try {
return mapper.readValue(json, MultiLayerConfiguration.class);
} catch (IOException e) {
throw new RuntimeException(e);
}
}
/**
* @return JSON representation of NN configuration
*/
public String toJson() {
ObjectMapper mapper = NeuralNetConfiguration.mapper();
synchronized (mapper) {
//JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields occasionally
//when writeValueAsString is used by multiple threads. This results in invalid JSON. See issue #3243
try {
return mapper.writeValueAsString(this);
} catch (org.nd4j.shade.jackson.core.JsonProcessingException e) {
throw new RuntimeException(e);
}
}
}
/**
* Create a neural net configuration from json
*
* @param json the neural net configuration from json
* @return {@link MultiLayerConfiguration}
*/
public static MultiLayerConfiguration fromJson(String json) {
MultiLayerConfiguration conf;
ObjectMapper mapper = NeuralNetConfiguration.mapper();
try {
conf = mapper.readValue(json, MultiLayerConfiguration.class);
} catch (InvalidTypeIdException e){
if(e.getMessage().contains("@class")){
try {
//JSON may be legacy (1.0.0-alpha or earlier), attempt to load it using old format
return JsonMappers.getLegacyMapper().readValue(json, MultiLayerConfiguration.class);
} catch (InvalidTypeIdException e2){
//Check for legacy custom layers: "Could not resolve type id 'CustomLayer' as a subtype of [simple type, class org.deeplearning4j.nn.conf.layers.Layer]: known type ids = [Bidirectional, CenterLossOutputLayer, CnnLossLayer, ..."
//1.0.0-beta5: dropping support for custom layers defined in pre-1.0.0-beta format. Built-in layers from these formats still work
String msg = e2.getMessage();
if(msg != null && msg.contains("Could not resolve type id")){
throw new RuntimeException("Error deserializing MultiLayerConfiguration - configuration may have a custom " +
"layer, vertex or preprocessor, in pre version 1.0.0-beta JSON format.\nModels in legacy format with custom" +
" layers should be loaded in 1.0.0-beta to 1.0.0-beta4 and saved again, before loading in the current version of DL4J", e);
}
throw new RuntimeException(e2);
} catch (IOException e2){
throw new RuntimeException(e2);
}
}
throw new RuntimeException(e);
} catch (IOException e) {
//Check if this exception came from legacy deserializer...
String msg = e.getMessage();
if (msg != null && msg.contains("legacy")) {
throw new RuntimeException("Error deserializing MultiLayerConfiguration - configuration may have a custom " +
"layer, vertex or preprocessor, in pre version 1.0.0-alpha JSON format. These layers can be " +
"deserialized by first registering them with NeuralNetConfiguration.registerLegacyCustomClassesForJSON(Class...)", e);
}
throw new RuntimeException(e);
}
//To maintain backward compatibility after loss function refactoring (configs generated with v0.5.0 or earlier)
// Previously: enumeration used for loss functions. Now: use classes
// IN the past, could have only been an OutputLayer or RnnOutputLayer using these enums
int layerCount = 0;
JsonNode confs = null;
for (NeuralNetConfiguration nnc : conf.getConfs()) {
Layer l = nnc.getLayer();
if (l instanceof BaseOutputLayer && ((BaseOutputLayer) l).getLossFn() == null) {
//lossFn field null -> may be an old config format, with lossFunction field being for the enum
//if so, try walking the JSON graph to extract out the appropriate enum value
BaseOutputLayer ol = (BaseOutputLayer) l;
try {
JsonNode jsonNode = mapper.readTree(json);
if (confs == null) {
confs = jsonNode.get("confs");
}
if (confs instanceof ArrayNode) {
ArrayNode layerConfs = (ArrayNode) confs;
JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
if (outputLayerNNCNode == null)
return conf; //Should never happen...
JsonNode outputLayerNode = outputLayerNNCNode.get("layer");
JsonNode lossFunctionNode = null;
if (outputLayerNode.has("output")) {
lossFunctionNode = outputLayerNode.get("output").get("lossFunction");
} else if (outputLayerNode.has("rnnoutput")) {
lossFunctionNode = outputLayerNode.get("rnnoutput").get("lossFunction");
}
if (lossFunctionNode != null) {
String lossFunctionEnumStr = lossFunctionNode.asText();
LossFunctions.LossFunction lossFunction = null;
try {
lossFunction = LossFunctions.LossFunction.valueOf(lossFunctionEnumStr);
} catch (Exception e) {
log.warn("OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON",
e);
}
if (lossFunction != null) {
switch (lossFunction) {
case MSE:
ol.setLossFn(new LossMSE());
break;
case XENT:
ol.setLossFn(new LossBinaryXENT());
break;
case NEGATIVELOGLIKELIHOOD:
ol.setLossFn(new LossNegativeLogLikelihood());
break;
case MCXENT:
ol.setLossFn(new LossMCXENT());
break;
//Remaining: TODO
case SQUARED_LOSS:
case RECONSTRUCTION_CROSSENTROPY:
default:
log.warn("OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not set loss function for {}",
lossFunction);
break;
}
}
}
} else {
log.warn("OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON: layer 'confs' field is not an ArrayNode (is: {})",
(confs != null ? confs.getClass() : null));
}
} catch (IOException e) {
log.warn("OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON",
e);
break;
}
}
//Also, pre 0.7.2: activation functions were Strings ("activationFunction" field), not classes ("activationFn")
//Try to load the old format if necessary, and create the appropriate IActivation instance
if ((l instanceof BaseLayer) && ((BaseLayer) l).getActivationFn() == null) {
try {
JsonNode jsonNode = mapper.readTree(json);
if (confs == null) {
confs = jsonNode.get("confs");
}
if (confs instanceof ArrayNode) {
ArrayNode layerConfs = (ArrayNode) confs;
JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
if (outputLayerNNCNode == null)
return conf; //Should never happen...
JsonNode layerWrapperNode = outputLayerNNCNode.get("layer");
if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
continue;
}
JsonNode layerNode = layerWrapperNode.elements().next();
JsonNode activationFunction = layerNode.get("activationFunction"); //Should only have 1 element: "dense", "output", etc
if (activationFunction != null) {
IActivation ia = Activation.fromString(activationFunction.asText()).getActivationFunction();
((BaseLayer) l).setActivationFn(ia);
}
}
} catch (IOException e) {
log.warn("Layer with null ActivationFn field or pre-0.7.2 activation function detected: could not parse JSON",
e);
}
}
if(!handleLegacyWeightInitFromJson(json, l, mapper, confs, layerCount)) {
return conf;
}
layerCount++;
}
return conf;
}
/**
* Handle {@link WeightInit} and {@link Distribution} from legacy configs in Json format. Copied from handling of {@link Activation}
* above.
* @return True if all is well and layer iteration shall continue. False else-wise.
*/
private static boolean handleLegacyWeightInitFromJson(String json, Layer l, ObjectMapper mapper, JsonNode confs, int layerCount) {
if ((l instanceof BaseLayer) && ((BaseLayer) l).getWeightInitFn() == null) {
try {
JsonNode jsonNode = mapper.readTree(json);
if (confs == null) {
confs = jsonNode.get("confs");
}
if (confs instanceof ArrayNode) {
ArrayNode layerConfs = (ArrayNode) confs;
JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
if (outputLayerNNCNode == null)
return false; //Should never happen...
JsonNode layerWrapperNode = outputLayerNNCNode.get("layer");
if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
return true;
}
JsonNode layerNode = layerWrapperNode.elements().next();
JsonNode weightInit = layerNode.get("weightInit"); //Should only have 1 element: "dense", "output", etc
JsonNode distribution = layerNode.get("dist");
Distribution dist = null;
if(distribution != null) {
dist = mapper.treeToValue(distribution, Distribution.class);
}
if (weightInit != null) {
final IWeightInit wi = WeightInit.valueOf(weightInit.asText()).getWeightInitFunction(dist);
((BaseLayer) l).setWeightInitFn(wi);
}
}
} catch (IOException e) {
log.warn("Layer with null WeightInit detected: " + l.getLayerName() + ", could not parse JSON",
e);
}
}
return true;
}
@Override
public String toString() {
return toJson();
}
public NeuralNetConfiguration getConf(int i) {
return confs.get(i);
}
@Override
public MultiLayerConfiguration clone() {
try {
MultiLayerConfiguration clone = (MultiLayerConfiguration) super.clone();
if (clone.confs != null) {
List list = new ArrayList<>();
for (NeuralNetConfiguration conf : clone.confs) {
list.add(conf.clone());
}
clone.confs = list;
}
if (clone.inputPreProcessors != null) {
Map map = new HashMap<>();
for (Map.Entry entry : clone.inputPreProcessors.entrySet()) {
map.put(entry.getKey(), entry.getValue().clone());
}
clone.inputPreProcessors = map;
}
clone.inferenceWorkspaceMode = this.inferenceWorkspaceMode;
clone.trainingWorkspaceMode = this.trainingWorkspaceMode;
clone.cacheMode = this.cacheMode;
clone.validateOutputLayerConfig = this.validateOutputLayerConfig;
clone.dataType = this.dataType;
return clone;
} catch (CloneNotSupportedException e) {
throw new RuntimeException(e);
}
}
public InputPreProcessor getInputPreProcess(int curr) {
return inputPreProcessors.get(curr);
}
/**
* Get a {@link MemoryReport} for the given MultiLayerConfiguration. This is used to estimate the
* memory requirements for the given network configuration and input
*
* @param inputType Input types for the network
* @return Memory report for the network
*/
public NetworkMemoryReport getMemoryReport(InputType inputType) {
Map memoryReportMap = new LinkedHashMap<>();
int nLayers = confs.size();
for (int i = 0; i < nLayers; i++) {
String layerName = confs.get(i).getLayer().getLayerName();
if (layerName == null) {
layerName = String.valueOf(i);
}
//Pass input type through preprocessor, if necessary
InputPreProcessor preproc = getInputPreProcess(i);
//TODO memory requirements for preprocessor
if (preproc != null) {
inputType = preproc.getOutputType(inputType);
}
LayerMemoryReport report = confs.get(i).getLayer().getMemoryReport(inputType);
memoryReportMap.put(layerName, report);
inputType = confs.get(i).getLayer().getOutputType(i, inputType);
}
return new NetworkMemoryReport(memoryReportMap, MultiLayerConfiguration.class, "MultiLayerNetwork", inputType);
}
/**
* For the given input shape/type for the network, return a list of activation sizes for each layer in the network.
* i.e., list.get(i) is the output activation sizes for layer i
*
* @param inputType Input type for the network
* @return A lits of activation types for the network, indexed by layer number
*/
public List getLayerActivationTypes(@NonNull InputType inputType) {
List out = new ArrayList<>();
int nLayers = confs.size();
for (int i = 0; i < nLayers; i++) {
InputPreProcessor preproc = getInputPreProcess(i);
if (preproc != null) {
inputType = preproc.getOutputType(inputType);
}
inputType = confs.get(i).getLayer().getOutputType(i, inputType);
out.add(inputType);
}
return out;
}
@Data
public static class Builder {
private static final int DEFAULT_TBPTT_LENGTH = 20;
protected List confs = new ArrayList<>();
protected double dampingFactor = 100;
protected Map inputPreProcessors = new HashMap<>();
protected BackpropType backpropType = BackpropType.Standard;
protected int tbpttFwdLength = DEFAULT_TBPTT_LENGTH;
protected int tbpttBackLength = DEFAULT_TBPTT_LENGTH;
protected InputType inputType;
protected WorkspaceMode trainingWorkspaceMode = WorkspaceMode.ENABLED;
protected WorkspaceMode inferenceWorkspaceMode = WorkspaceMode.ENABLED;
protected CacheMode cacheMode = CacheMode.NONE;
protected boolean validateOutputConfig = true;
protected boolean validateTbpttConfig = true;
protected DataType dataType;
protected boolean overrideNinUponBuild = true;
/**
* Whether to over ride the nIn
* configuration forcibly upon construction.
* Default value is true
* @param overrideNinUponBuild Whether to over ride the nIn
* configuration forcibly upon construction.
* @return builder pattern
*/
public Builder overrideNinUponBuild(boolean overrideNinUponBuild) {
this.overrideNinUponBuild = overrideNinUponBuild;
return this;
}
/**
* Specify the processors.
* These are used at each layer for doing things like normalization and
* shaping of input.
*
* @param processor what to use to preProcess the data.
* @return builder pattern
*/
public Builder inputPreProcessor(Integer layer, InputPreProcessor processor) {
inputPreProcessors.put(layer, processor);
return this;
}
public Builder inputPreProcessors(Map processors) {
this.inputPreProcessors = processors;
return this;
}
/**
* @deprecated Use {@link NeuralNetConfiguration.Builder#trainingWorkspaceMode(WorkspaceMode)}
*/
@Deprecated
public Builder trainingWorkspaceMode(@NonNull WorkspaceMode workspaceMode) {
this.trainingWorkspaceMode = workspaceMode;
return this;
}
/**
* @deprecated Use {@link NeuralNetConfiguration.Builder#inferenceWorkspaceMode(WorkspaceMode)}
*/
@Deprecated
public Builder inferenceWorkspaceMode(@NonNull WorkspaceMode workspaceMode) {
this.inferenceWorkspaceMode = workspaceMode;
return this;
}
/**
* This method defines how/if preOutput cache is handled:
* NONE: cache disabled (default value)
* HOST: Host memory will be used
* DEVICE: GPU memory will be used (on CPU backends effect will be the same as for HOST)
*
* @param cacheMode
* @return
*/
public Builder cacheMode(@NonNull CacheMode cacheMode) {
this.cacheMode = cacheMode;
return this;
}
/**
* The type of backprop. Default setting is used for most networks (MLP, CNN etc),
* but optionally truncated BPTT can be used for training recurrent neural networks.
* If using TruncatedBPTT make sure you set both tBPTTForwardLength() and tBPTTBackwardLength()
*/
public Builder backpropType(@NonNull BackpropType type) {
this.backpropType = type;
return this;
}
/**
* When doing truncated BPTT: how many steps should we do?
* Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
* See: http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
*
* @param bpttLength length > 0
*/
public Builder tBPTTLength(int bpttLength) {
tBPTTForwardLength(bpttLength);
return tBPTTBackwardLength(bpttLength);
}
/**
* When doing truncated BPTT: how many steps of forward pass should we do
* before doing (truncated) backprop?
* Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
* Typically tBPTTForwardLength parameter is same as the tBPTTBackwardLength parameter,
* but may be larger than it in some circumstances (but never smaller)
* Ideally your training data time series length should be divisible by this
* This is the k1 parameter on pg23 of
* http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
*
* @param forwardLength Forward length > 0, >= backwardLength
*/
public Builder tBPTTForwardLength(int forwardLength) {
this.tbpttFwdLength = forwardLength;
return this;
}
/**
* When doing truncated BPTT: how many steps of backward should we do?
* Only applicable when doing backpropType(BackpropType.TruncatedBPTT)
* This is the k2 parameter on pg23 of
* http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf
*
* @param backwardLength <= forwardLength
*/
public Builder tBPTTBackwardLength(int backwardLength) {
this.tbpttBackLength = backwardLength;
return this;
}
public Builder confs(List confs) {
this.confs = confs;
return this;
}
public Builder setInputType(InputType inputType) {
this.inputType = inputType;
return this;
}
/**
* Enabled by default. If enabled, the output layer configuration will be validated, to throw an exception on
* likely invalid outputs - such as softmax + nOut=1, or LossMCXENT + Tanh.
* If disabled (false) no output layer validation will be performed.
* Disabling this validation is not recommended, as the configurations that fail validation usually will
* not be able to learn correctly. However, the option to disable this validation is provided for advanced users
* when creating non-standard architectures.
*
* @param validate If true: validate output layer configuration. False: don't validate
*/
public Builder validateOutputLayerConfig(boolean validate) {
this.validateOutputConfig = validate;
return this;
}
/**
* Enabled by default. If enabled, an exception will be throw when using the (invalid) combination of truncated
* backpropagation through time (TBPTT) with either a GlobalPoolingLayer or LastTimeStepLayer.
* It is possible to disable this validation to allow what is almost certainly an invalid configuration to be used,
* however this is not recommended.
*
* @param validate Whether TBPTT validation should be performed
*/
public Builder validateTbpttConfig(boolean validate){
this.validateTbpttConfig = validate;
return this;
}
/**
* Set the DataType for the network parameters and activations for all layers in the network. Default: Float
* @param dataType Datatype to use for parameters and activations
*/
public Builder dataType(@NonNull DataType dataType){
this.dataType = dataType;
return this;
}
public MultiLayerConfiguration build() {
//Validate BackpropType setting
if ((tbpttBackLength != DEFAULT_TBPTT_LENGTH || tbpttFwdLength != DEFAULT_TBPTT_LENGTH) && backpropType != BackpropType.TruncatedBPTT) {
log.warn("Truncated backpropagation through time lengths have been configured with values " + tbpttFwdLength
+ " and " + tbpttBackLength + " but backprop type is set to " + backpropType + ". TBPTT configuration" +
" settings will only take effect if backprop type is set to BackpropType.TruncatedBPTT");
}
if(backpropType == BackpropType.TruncatedBPTT && validateTbpttConfig) {
//Check for invalid combination - tbptt plus LastTimeStepLayer or
for( int i = 0; i < confs.size(); i++) {
Layer l = confs.get(i).getLayer();
if(l instanceof LastTimeStep || l instanceof GlobalPoolingLayer){
throw new IllegalStateException("Invalid network configuration detected: Truncated backpropagation through time (TBPTT)" +
" cannot be used with layer " + i + " of type " + l.getClass().getName() + ": TBPTT is incompatible with this layer type (which is designed " +
"to process entire sequences at once, and does support the type of sequence segments that TPBTT uses).\n" +
"This check can be disabled using validateTbpttConfig(false) but this is not recommended.");
}
}
}
if (inputType == null && inputPreProcessors.get(0) == null) {
//User hasn't set the InputType. Sometimes we can infer it...
// For example, Dense/RNN layers, where preprocessor isn't set -> user is *probably* going to feed in
// standard feedforward or RNN data
//This isn't the most elegant implementation, but should avoid breaking backward compatibility here
//Can't infer InputType for CNN layers, however (don't know image dimensions/depth)
Layer firstLayer = confs.get(0).getLayer();
if (firstLayer instanceof BaseRecurrentLayer) {
BaseRecurrentLayer brl = (BaseRecurrentLayer) firstLayer;
val nIn = brl.getNIn();
if (nIn > 0) {
inputType = InputType.recurrent(nIn, brl.getRnnDataFormat());
}
} else if (firstLayer instanceof DenseLayer || firstLayer instanceof EmbeddingLayer
|| firstLayer instanceof OutputLayer) {
//Can't just use "instanceof FeedForwardLayer" here. ConvolutionLayer is also a FeedForwardLayer
FeedForwardLayer ffl = (FeedForwardLayer) firstLayer;
val nIn = ffl.getNIn();
if (nIn > 0) {
inputType = InputType.feedForward(nIn);
}
}
}
//Add preprocessors and set nIns, if InputType has been set
// Builder.inputType field can be set in 1 of 4 ways:
// 1. User calls setInputType directly
// 2. Via ConvolutionLayerSetup -> internally calls setInputType(InputType.convolutional(...))
// 3. Via the above code: i.e., assume input is as expected by the RNN or dense layer -> sets the inputType field
if (inputType != null) {
InputType currentInputType = inputType;
for (int i = 0; i < confs.size(); i++) {
Layer l = confs.get(i).getLayer();
if (inputPreProcessors.get(i) == null) {
//Don't override preprocessor setting, but set preprocessor if required...
InputPreProcessor inputPreProcessor = l.getPreProcessorForInputType(currentInputType);
if (inputPreProcessor != null) {
inputPreProcessors.put(i, inputPreProcessor);
}
}
InputPreProcessor inputPreProcessor = inputPreProcessors.get(i);
if (inputPreProcessor != null) {
currentInputType = inputPreProcessor.getOutputType(currentInputType);
}
if(i > 0) {
Layer layer = confs.get(i - 1).getLayer();
//convolution 1d is an edge case where it has rnn input type but the filters
//should be the output
if(layer instanceof Convolution1DLayer) {
if(l instanceof DenseLayer && inputType instanceof InputType.InputTypeRecurrent) {
FeedForwardLayer feedForwardLayer = (FeedForwardLayer) l;
if(inputType instanceof InputType.InputTypeRecurrent) {
InputType.InputTypeRecurrent recurrent = (InputType.InputTypeRecurrent) inputType;
feedForwardLayer.setNIn(recurrent.getTimeSeriesLength());
}
}
else
l.setNIn(currentInputType, overrideNinUponBuild); //Don't override the nIn setting, if it's manually set by the user
}
else
l.setNIn(currentInputType, overrideNinUponBuild); //Don't override the nIn setting, if it's manually set by the user
}
else
l.setNIn(currentInputType, overrideNinUponBuild); //Don't override the nIn setting, if it's manually set by the user
currentInputType = l.getOutputType(i, currentInputType);
}
}
MultiLayerConfiguration conf = new MultiLayerConfiguration();
conf.confs = this.confs;
conf.inputPreProcessors = inputPreProcessors;
conf.backpropType = backpropType;
conf.tbpttFwdLength = tbpttFwdLength;
conf.tbpttBackLength = tbpttBackLength;
conf.trainingWorkspaceMode = trainingWorkspaceMode;
conf.inferenceWorkspaceMode = inferenceWorkspaceMode;
conf.cacheMode = cacheMode;
conf.dataType = dataType;
Nd4j.getRandom().setSeed(conf.getConf(0).getSeed());
//Validate output layer configuration
if (validateOutputConfig) {
//Validate output layer configurations...
for (NeuralNetConfiguration n : conf.getConfs()) {
Layer l = n.getLayer();
OutputLayerUtil.validateOutputLayer(l.getLayerName(), l); //No-op for non output/loss layers
}
}
return conf;
}
}
}
